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Duration 14 hours
Course Outline
Introduction to Nano Banana
- An overview of the framework and its key capabilities
- Understanding the architecture and processing pipeline
- Comparing Nano Banana against other on-device AI solutions
Setting Up the Development Environment
- Configuring Android Studio for AI workloads
- Integrating the Nano Banana SDK
- Managing project configuration and dependencies
Utilizing Nano Banana APIs
- Exploring core API methods
- Loading and managing lightweight models
- Executing inference tasks in real time
Optimizing AI Performance on Android
- Strategies for achieving low-latency inference
- Techniques for effective memory and resource management
- Approaches to benchmarking and optimization tools
Designing AI-Driven User Experiences
- Implementing responsive UI interactions
- Handling asynchronous tasks and callbacks
- Aligning AI behaviors with Android UX guidelines
Security and Privacy in On-Device AI
- Ensuring the secure handling of user data
- Techniques for privacy-preserving inference
- Compliance considerations for enterprise deployments
Deploying and Maintaining AI Features
- Packaging and publishing applications with embedded AI
- Managing versioning and updates for local models
- Monitoring and enhancing performance after deployment
Advanced Use Cases and Integrations
- Combining Nano Banana with existing Android ML tools
- Implementing multimodal AI features
- Extending applications with custom lightweight models
Summary and Next Steps
Requirements
- A solid grasp of Android application fundamentals
- Proficiency in either Kotlin or Java
- A basic understanding of mobile app debugging workflows
Target Audience
- Android developers focused on building AI-enhanced applications
- Software engineers investigating on-device ML workflows
- Technical teams assessing lightweight AI deployment strategies on Android
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